Academic Journal
Evaluation method for driver comfort under multi axis coherent vibration of seats.
| Τίτλος: | Evaluation method for driver comfort under multi axis coherent vibration of seats. |
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| Συγγραφείς: | Bai X; School of Mechanical Engineering, North University of China, Taiyuan, China., Huang J; School of Mechanical Engineering, North University of China, Taiyuan, China., Yang L; China North Vehicle Research Institute, Beijing, China.; National Laboratory for Wheeled Vehicles, Beijing, China., Wang Y; School of Mechanical Engineering, North University of China, Taiyuan, China., Deng G; China North Vehicle Research Institute, Beijing, China.; National Laboratory for Wheeled Vehicles, Beijing, China., Fan Z; School of Mechanical Engineering, North University of China, Taiyuan, China., Yang F; School of Mechanical Engineering, North University of China, Taiyuan, China. |
| Πηγή: | Ergonomics [Ergonomics] 2026 Oct; Vol. 69 (10), pp. 2102-2123. Date of Electronic Publication: 2025 Sep 03. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Informa Healthcare Country of Publication: England NLM ID: 0373220 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1366-5847 (Electronic) Linking ISSN: 00140139 NLM ISO Abbreviation: Ergonomics Subsets: MEDLINE |
| Imprint Name(s): | Publication: London : Informa Healthcare Original Publication: London, Taylor & Francis. |
| Ιατρικοί όροι (MeSH): | Ergonomics*/methods , Automobile Driving*/psychology , Vibration* , Equipment Design* , Deep Learning*, Humans ; Male ; Adult ; Female ; Young Adult |
| Περίληψη: | Ergonomics increasingly emphasises that seat design should align with the driver's physiological needs to enhance comfort and health. This study uses deep learning to evaluate the impact of seat multi-axis coherent vibration on driver comfort. Through road tests, the multi-axis vibration signals were collected from the seat backrest, cushion and floor, simultaneously collecting subjective evaluation data. The consistency between subjective and objective data was verified using Stevens' power law, with the R2 exceeding 70%, indicating subjective evaluations can reflect driver comfort. Furthermore, a deep learning model integrating multimodal coherent features was used for quantitative evaluation. The results show that the method accurately captures frequency characteristics affecting comfort, with the metrics R2, RMSE and MAE being 0.931, 0.096 and 0.071, respectively. This is comparable to the evaluations of ergonomics experts. The proposed method provides a promising solution for driver comfort evaluation. It is significant for enhancing driver health, comfort and safety. |
| Contributed Indexing: | Keywords: Comfort evaluation; deep learning; driver seat; multiaxial vibration; vibration comfort Local Abstract: [plain-language-summary] Driver comfort has increasingly become a focus of attention; however, current evaluation methods do not accurately reflect driver discomfort. Based on road tests, this article presents a quick and precise comfort evaluation method, unveiling the nonlinear mapping relationship between multi-axis seat vibrations and driver comfort. |
| Entry Date(s): | Date Created: 20250903 Date Completed: 20260907 Latest Revision: 20260907 |
| Update Code: | 20260907 |
| DOI: | 10.1080/00140139.2025.2553136 |
| PMID: | 40901779 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1366-5847 |
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| DOI: | 10.1080/00140139.2025.2553136 |